Graph-Theoretic Approaches to Targeted Marketing Strategies in Social Networks

Abstract The rapid expansion of large-scale social networking platforms has significantly reshaped modern marketing practices, shifting the focus from broad-based advertising toward precisely targeted promotional strategies. A key challenge in targeted marketing lies in identifying individuals capable of exerting substantial influence on information diffusion and consumer decision-making. Since social networks fundamentally consist of relational interactions, graph theory provides a natural and mathematically rigorous framework for their analysis. In this study, social networks are represented as graphs, where users correspond to vertices and social interactions are modeled as edges. Classical graph-theoretic metrics—including degree centrality, betweenness centrality, clustering coefficient, and eigenvector centrality—are employed to examine structural influence patterns relevant to marketing applications. A systematic graph-based analytical framework is proposed and demonstrated through two illustrative graph models. The analysis reveals that distinct centrality measures highlight different categories of influential users, and reliance on a single metric may result in ineffective targeting decisions. The findings emphasize the applicability of graph theory in applied mathematics and underscore its relevance for interdisciplinary research involving social and economic networks.